Industrial vehicle with feature-based localization and navigation

ABSTRACT

An industrial vehicle is provided comprising a drive mechanism, a steering mechanism, a vehicle controller, a camera, and a navigation module. The camera is communicatively coupled to the navigation module, the vehicle controller is responsive to commands from the navigation module, and the drive mechanism and the steering mechanism are responsive to commands from the vehicle controller. The camera is configured to capture an input image of a warehouse ceiling comprising elongated skylights characterized by different rates of image intensity change along longitudinal and transverse axial directions, and ceiling lights characterized by a circularly symmetric rate of image intensity change. The navigation module is configured to distinguish between the ceiling lights and the skylights and send commands to the vehicle controller for localization, or to navigate the industrial vehicle through the warehouse based upon valid ceiling light identification, valid skylight identification, or both.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims priority as a continuation to U.S.Non-Provisional application Ser. No. 15/255,785 (CRNZ 0057 PA), filedSep. 2, 2016, which claims priority to U.S. Provisional Application Ser.Nos. 62/214,445 (CRNZ 0057 MA), filed Sep. 4, 2015, and 62/219,259 (CRNZ0057 M2), filed Sep. 16, 2015.

BACKGROUND Field

The present disclosure relates to systems and methods for providingillumination-invariant feature detection and, more specifically, tosystems and methods for providing illumination-invariant functions forfeature detection.

Technical Background

In order to move items about an industrial environment, workers oftenutilize industrial vehicles, including for example, forklift trucks,hand and motor driven pallet trucks, and/or other materials handlingvehicles. The industrial vehicles can be configured as an automatedguided vehicle or a manually guided vehicle that navigates through theenvironment. In order to facilitate automated guidance, or any type ofvehicle navigation, the industrial vehicle may be adapted forlocalization within the environment. That is, the industrial vehicle canbe adapted with sensors and processors for localization, i.e.,determining the location and, optionally, the pose of the industrialvehicle within the environment. The sensors can be configured to detectobjects in the environment and the localization can be dependent uponfeatures extracted from such detected objects. Systems of this natureare described in, for example, US PG Pub. Nos. 2016/0090281 and2016/0011595.

BRIEF SUMMARY

In accordance with one embodiment of the present disclosure, anindustrial vehicle is provided comprising a drive mechanism, a steeringmechanism, a vehicle controller, a camera, and a navigation module. Thecamera is communicatively coupled to the navigation module, the vehiclecontroller is responsive to commands from the navigation module, and thedrive mechanism and the steering mechanism are responsive to commandsfrom the vehicle controller. The camera is configured to capture aninput image of a warehouse ceiling comprising elongated skylightscharacterized by different rates of image intensity change alonglongitudinal and transverse axial directions, and ceiling lightscharacterized by a circularly symmetric rate of image intensity change.The navigation module is configured to distinguish between the ceilinglights and the skylights and send commands to the vehicle controller tonavigate the industrial vehicle through the warehouse based upon validceiling light identification, valid skylight identification, or both.

More specifically, it is contemplated that the navigation module mayexecute machine readable instructions to create a Gaussian scale spacepyramid from the input image of the warehouse ceiling, wherein theGaussian scale space pyramid comprises a plurality of scale spaceimages. The module calculates a determinant of Hessian response for eachimage within the Gaussian scale space pyramid and builds a determinantof Hessian response pyramid of the same size and structure as theGaussian scale space pyramid. The module also calculates a trace ofHessian response for each image within the Gaussian scale space pyramidand builds a trace of Hessian response pyramid of the same size andstructure as the Gaussian scale space pyramid. The module utilizes thedeterminant of Hessian response pyramid to identify ceiling lightcandidates in the input image of the warehouse ceiling, and the trace ofHessian response pyramid to identify skylight candidates in the inputimage of the warehouse ceiling. The ceiling light candidates aresubjected to ceiling light candidate feature processing to identifyvalid ceiling lights in the warehouse, and the skylight candidates aresubjected to skylight candidate feature processing to identify validskylights in the warehouse.

It is contemplated that the Gaussian scale space pyramid can be createdby running a cascading series of image smoothing operations applied tothe input image of the warehouse ceiling and can be approximated byconvolution with binomial filter kernels. It is also contemplated thatthe Gaussian scale space pyramid can be created by supplementing thecascading series of image smoothing operations with subsamplingoperations. The supplemental subsampling operations can be implementedconditionally as a function of available navigation module computingpower.

The determinant of Hessian response and trace of Hessian response can becalculated based on each scale space image being convolved withsecond-order partial derivative filter kernels. It is also contemplatedthat the determinant of Hessian response can be calculated bysubtraction of a mixed second-order partial derivative term. Thedeterminant of Hessian response suppresses responses to objectscharacterized by different rates of image intensity change alonglongitudinal and transverse axial directions.

The determinant response can be utilized for multiscale non-maximumsuppression wherein local maxima are located within a window comprisingscale and spatial dimensions. In which case, an absolute minimumthreshold can be applied to prevent excessive noisy false positivedeterminant responses. The determinant response can be utilized in afiltering keypoints function for removing candidate points that do notlikely correspond to ceiling lights. The filtering keypoints functionmay utilize other filters on candidate points to check againstempirically set thresholds. The other filters may comprise scales atwhich keypoints are detected, the spatial location of a keypoint, amagnitude of the determinant of Hessian response at the keypointlocation, a machine-learning classifier, or an average trace of Hessianresponse values of the surrounding area. Finally, it is contemplatedthat the determinant response may be utilized in a refining keypointsfunction for refining spatial position and scale of ceiling lightcandidate keypoints.

The trace response can be utilized in a sum large-scale trace of Hessianresponse images function for summing a selection of trace of Hessianresponse images from a trace of Hessian response pyramid into anintegrated trace of Hessian response image. It is contemplated thatskylight regions can be searched for within the trace of Hessianresponse pyramid. The trace response may be further processed by athreshold integrated trace of Hessian response function, wherein thetrace of Hessian response is smoothed and a threshold is applied. Thethreshold may be fixed, or not. It is also contemplated that the traceof Hessian response can be utilized in a connected component filteringfunction for extracting and filtering connected components based on thebinary thresholded integrated trace of Hessian response image. Theconnected component filtering function may filter for size and aspectratio to select substantially rectangular regions.

In accordance with another embodiment of the present disclosure, thenavigation module is configured to distinguish between the ceilinglights and the skylights and send commands to the vehicle controller tolocalize the industrial vehicle through the warehouse based upon validceiling light identification, valid skylight identification, or both.

BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWINGS

The following detailed description of specific embodiments of thepresent disclosure can be best understood when read in conjunction withthe following drawings, where like structure is indicated with likereference numerals and in which:

FIG. 1 depicts a vehicle for environmental based localization accordingto one or more embodiments shown and described herein;

FIG. 2 depicts a flowchart of an exemplary algorithm for camera featureextraction/overhead lighting feature extraction for environmental basedlocalization according to one or more embodiments shown and describedherein;

FIG. 3 schematically depicts an input image showing three skylightsaccording to one or more embodiments shown and described herein;

FIGS. 4A and 4B schematically depict input images and thresholded imagesshowing multiple rows of skylights and multiple round lights accordingto one or more embodiments shown and described herein;

FIG. 5 depicts a flowchart of an exemplary algorithm forillumination-invariant feature detection of round lights and skylightsaccording to one or more embodiments shown and described herein;

FIG. 6 schematically depicts a scale space pyramid according to one ormore embodiments shown and described herein;

FIG. 7 schematically depicts multiscale non-maximum suppressionaccording to one or more embodiments shown and described herein;

FIGS. 8A and 8B schematically depict detected feature candidatesoverlaid on the input image of FIGS. 4A and 4B according to one or moreembodiments shown and described herein; and

FIG. 9 depicts a flowchart of an exemplary algorithm for ceiling lightcandidate feature detection according to one or more embodiments shownand described herein; and

FIG. 10 depicts a flowchart of an exemplary algorithm for skylightcandidate feature regions of interest detection according to one or moreembodiments shown and described herein;

FIG. 11 depicts a flowchart of an exemplary algorithm for skylightextraction according to one or more embodiments shown and describedherein;

FIG. 12 schematically depicts oriented bounding boxes over skylightimages overlaid upon the input image of FIG. 3 according to one or moreembodiments shown and described herein;

FIG. 13 schematically depicts an axis-aligned region of interest overskylight images overlaid upon the input image of FIG. 3 according to oneor more embodiments shown and described herein;

FIGS. 14A and 14B schematically depict line segment selection overlaidupon the input image of FIG. 3 according to one or more embodimentsshown and described herein;

FIG. 15 schematically depicts line segment pairing selection overlaidupon the input image of FIG. 3 according to one or more embodimentsshown and described herein;

FIGS. 16A and 16B schematically depict centerline features overlaid uponthe input image of FIG. 3 according to one or more embodiments shown anddescribed herein;

FIG. 17 schematically depicts an input image showing a row of skylightsaccording to one or more embodiments shown and described herein;

FIG. 18 schematically depicts edge lines of the input image of FIG. 17according to one or more embodiments shown and described herein;

FIGS. 19A and 19B schematically depict edge lines and regions ofinterest overlaid upon the input image of FIG. 17 according to one ormore embodiments shown and described herein;

FIG. 20 depicts a flowchart of an exemplary algorithm for extractingpoint fixes according to one or more embodiments shown and describedherein;

FIGS. 21A and 21B schematically depict transversal edges of the inputimage of FIG. 17 according to one or more embodiments shown anddescribed herein;

FIGS. 22A and 22B schematically depict usable and unusable edges of thetransversal edges of FIGS. 21A and 21B according to one or moreembodiments shown and described herein; and

FIG. 23 schematically depicts point fixes overlaid upon the input imageof FIG. 17 according to one or more embodiments shown and describedherein.

DETAILED DESCRIPTION

The embodiments described herein generally relate to Environmental BasedLocalization techniques (EBL) for extracting features from overheadlighting including, but not limited to, skylights. The EBL may be usedto localize and/or navigate an industrial vehicle through a buildingstructure, such as a warehouse. Suitably, the overhead lighting may bemounted in or on a ceiling of a building. However, in some embodimentsthe lighting may also or alternatively be suspended from a ceiling orwall via suitable structure. In some embodiments, a camera can bemounted to an industrial vehicle (e.g., automated guided vehicle or amanually guided vehicle) that navigates through a warehouse. The inputimage can be any image captured from the camera prior to extractingfeatures from the image.

Referring initially to FIG. 1, a vehicle 100 can be configured tonavigate through a warehouse 110. The vehicle 100 can comprise anindustrial vehicle for lifting and moving a payload such as, forexample, a forklift truck, a reach truck, a turret truck, a walkiestacker truck, a tow tractor, a pallet truck, a high/low, astacker-truck, trailer loader, a sideloader, a fork hoist, or the like.The industrial vehicle can be configured to automatically or manuallynavigate a surface 122 of the warehouse 110 along a desired path.Accordingly, the vehicle 100 can be directed forwards and backwards byrotation of one or more wheels 124. Additionally, the vehicle 100 can becaused to change direction by steering the one or more wheels 124.Optionally, the vehicle can comprise operator controls 126 forcontrolling functions of the vehicle such as, but not limited to, thespeed of the wheels 124, the orientation of the wheels 124, or the like.The operator controls 126 can comprise controls that are assigned tofunctions of the vehicle 100 such as, for example, switches, buttons,levers, handles, pedals, input/output device, or the like. It is notedthat the term “navigate” as used herein can mean controlling themovement of a vehicle from one place to another.

The vehicle 100 can further comprise a camera 102 for capturing overheadimages. The camera 102 can be any device capable of capturing the visualappearance of an object and transforming the visual appearance into animage. Accordingly, the camera 102 can comprise an image sensor such as,for example, a charge coupled device, complementarymetal-oxide-semiconductor sensor, or functional equivalents thereof. Insome embodiments, the vehicle 100 can be located within the warehouse110 and be configured to capture overhead images of the ceiling 112 ofthe warehouse 110. In order to capture overhead images, the camera 102can be mounted to the vehicle 100 and focused to the ceiling 112. Forthe purpose of defining and describing the present disclosure, the term“image” as used herein can mean a representation of the appearance of adetected object. The image can be provided in a variety of machinereadable representations such as, for example, JPEG, JPEG 2000, Exif,TIFF, raw image formats, GIF, BMP, PNG, Netpbm format, WEBP, rasterformats, vector formats, or any other format suitable for capturingoverhead objects.

The ceiling 112 of the warehouse 110 can comprise overhead lights suchas, but not limited to, ceiling lights 114 for providing illuminationfrom the ceiling 112 or generally from above a vehicle operating in thewarehouse. The ceiling lights 114 can comprise substantially rectangularlights such as, for example, skylights 116, fluorescent lights, or thelike; and may be mounted in or suspended from the ceiling or wallstructures so as to provide illumination from above. As used herein, theterm “skylight” can mean an aperture in a ceiling or roof fitted with asubstantially light transmissive medium for admitting daylight, such as,for example, air, glass, plastic or the like. While skylights can comein a variety of shapes and sizes, the skylights described herein caninclude “standard” long, substantially rectangular skylights that may ormay not be split by girders or crossbars into a series of panels.Alternatively, skylights can comprise smaller, discrete skylights ofrectangular or circular shape that are similar in size to a bedroomwindow, i.e., about 30 inches by about 60 inches (about 73 cm by about146 cm). Alternatively or additionally, the ceiling lights 114 cancomprise substantially circular lights such as, for example, roundlights 118, merged lights 120, which can comprise a plurality ofadjacent round lights that appear to be a single object, or the like.Thus, overhead lights or “ceiling lights” include sources of natural(e.g. sunlight) and artificial (e.g. electrically powered) light. Insome embodiments, the ceiling lights are circularly symmetric roundlights and the skylights are substantially elongated. In geometry,circular symmetry is a type of continuous symmetry for a planar objectthat can be rotated by any arbitrary angle and map onto itself.

The embodiments described herein can comprise one or more processors 104communicatively coupled to the camera 102. The one or more processors104 can execute machine readable instructions to implement any of themethods or functions described herein automatically. Memory 106 forstoring machine readable instructions can be communicatively coupled tothe one or more processors 104, the camera 102, or any combinationthereof. The one or more processors 104 can comprise a processor, anintegrated circuit, a microchip, a computer, or any other computingdevice capable of executing machine readable instructions or that hasbeen configured to execute functions in a manner analogous to machinereadable instructions. The memory 106 can comprise RAM, ROM, a flashmemory, a hard drive, or any non-transitory device capable of storingmachine readable instructions.

The one or more processors 104 and the memory 106 may be integral withthe camera 102. Alternatively or additionally, each of the one or moreprocessors 104 and the memory 106 can be integral with the vehicle 100.Moreover, each of the one or more processors 104 and the memory 106 canbe separated from the vehicle 100 and the camera 102. For example, aserver or a mobile computing device can comprise the one or moreprocessors 104, the memory 106, or both. It is noted that the one ormore processors 104, the memory 106, and the camera 102 may be discretecomponents communicatively coupled with one another without departingfrom the scope of the present disclosure. Accordingly, in someembodiments, components of the one or more processors 104, components ofthe memory 106, and components of the camera 102 can be physicallyseparated from one another. The phrase “communicatively coupled,” asused herein, means that components are capable of exchanging datasignals with one another such as, for example, electrical signals viaconductive medium, electromagnetic signals via air, optical signals viaoptical waveguides, or the like.

Thus, embodiments of the present disclosure may comprise logic or analgorithm written in any programming language of any generation (e.g.,1GL, 2GL, 3GL, 4GL, or 5GL). The logic or an algorithm can be written asmachine language that may be directly executed by the processor, orassembly language, object-oriented programming (OOP), scriptinglanguages, microcode, etc., that may be compiled or assembled intomachine readable instructions and stored on a machine readable medium.Alternatively or additionally, the logic or algorithm may be written ina hardware description language (HDL). Further, the logic or algorithmcan be implemented via either a field-programmable gate array (FPGA)configuration or an application-specific integrated circuit (ASIC), ortheir equivalents.

As is noted above, the vehicle 100 can comprise or be communicativelycoupled with the one or more processors 104. Accordingly, the one ormore processors 104 can execute machine readable instructions to operateor replace the function of the operator controls 126. The machinereadable instructions can be stored upon the memory 106. Accordingly, insome embodiments, the vehicle 100 can be navigated automatically by theone or more processors 104 executing the machine readable instructions.In some embodiments, the location of the vehicle can be monitored by theEBL as the vehicle 100 is navigated.

For example, the vehicle 100 can automatically navigate along thesurface 122 of the warehouse 110 along a desired path to a desiredposition based upon a localized position of the vehicle 100. In someembodiments, the vehicle 100 can determine the localized position of thevehicle 100 with respect to the warehouse 110. The determination of thelocalized position of the vehicle 100 can be performed by comparingimage data to map data. The map data can be stored locally in the memory106, which can be updated periodically, or map data provided by a serveror the like. Given the localized position and the desired position, atravel path can be determined for the vehicle 100. Once the travel pathis known, the vehicle 100 can travel along the travel path to navigatethe surface 122 of the warehouse 110. Specifically, the one or moreprocessors 104 can execute machine readable instructions to perform EBLfunctions and operate the vehicle 100. In one embodiment, the one ormore processors 104 can adjust the steering of the wheels 124 andcontrol the throttle to cause the vehicle 100 to navigate the surface122.

Referring now to FIG. 2, a flow chart of a sequence of functions for afull Camera Feature Extraction (CFE) algorithm 10 is schematicallydepicted. It is noted that, while the functions are enumerated anddepicted as being performed in a particular sequence in the depictedembodiment, the functions can be performed in an alternative orderwithout departing from the scope of the present disclosure. It isfurthermore noted that one or more of the functions can be omittedwithout departing from the scope of the embodiments described herein.

Referring collectively to FIGS. 1-3, the CFE algorithm 10 can comprise apreprocess function 20 for processing the input image 200 of the ceiling112 prior to executing further functions on the input image 200. Theinput image 200 is depicted as having captured a frame that includesskylights 116 corresponding to the skylights 116 of a warehouse 110. Insome embodiments, the preprocess function 20 can comprise functions forthe removal of lens distortion effects from the input image 200.Alternatively or additionally, the input image 200 can be captured bydeliberately underexposing the input image 200 in order to highlight theceiling lights 114, which can include skylights 116. It has beendiscovered that low exposure can aid in reducing reflections and otherspurious artifacts, which can make the feature extraction processingmore complicated and less reliable.

The CFE algorithm 10 can further comprise a feature detection function22 for detecting low-level features from the input image 200 of theceiling 112. The feature detection function 22 can utilize one or morefeature detection algorithms such as, for example, maximally stableextremal regions (MSER) algorithm, a thresholding step combined withOtsu's method to extract raw features (i.e. lights) from the image, anillumination-invariant low-level feature detector utilizing differentialimages, or equivalent algorithms. The raw low-level features may consistof points, connected pixel regions defined by bounding boxes, or thelike, and indicate candidate locations for further processing todetermine features sufficient for localization. Specifically, thedetected features from the input images 200 can be utilized by thelocalization process to determine the positions of ceiling lights 114that are captured by the camera 102. For example, centroids can beextracted from round lighting features such as substantially circularshaped lights. Additionally, for smaller skylights the full extent ofthe smaller skylight can be captured within a single image frame.Accordingly, a centroid extraction function can be applied, as persubstantially circular lights. Additionally or alternatively, corners130 or point fixes can be extracted from longer or larger substantiallyrectangular lights, such as long skylights consisting of a series ofpanels separated by roofing structure.

Referring collectively to FIGS. 1-3, during the feature detectionfunction 22 to determine candidate feature regions to input into theprocess round lights function 26, process merged lights function 28 andprocess skylights function 30, algorithms such as thresholding withOtsu's method can fail for EBL purposes when the appearance of warehouseceiling lights 114 in the input image 200 are not of similar brightness.

Certain feature detection processes 22 such as Otsu's method can findthe optimum global image threshold in terms of maximizing theinter-class (foreground and background pixel classes) variance. Otsu'smethod is fully described in the work, “A threshold selection methodfrom gray-level histograms.” Automatica, 11(285-296), 23-27 (1975). Thisallows the global image threshold to be chosen with some degree offlexibility and robustness, as opposed to manually fixing a threshold byhand at each site.

Using Otsu's method to determine thresholds across the EBL sites ensuresthe threshold will vary according to the brightness of the ceilinglights 114 at each site. This approach is most appropriate for locationswith artificial lights, all of similar brightness. In locations wherelarge variations in illumination intensity are present, such as iscommon with skylights 116, another approach may be more accurate.Specifically, using the Otsu or similar method to extract feature may beless than optimal if there are multiple types of lighting with differentbrightness levels, or the resultant global image threshold is unsuitablefor the dimmer portions of the input image 200 and desirable featuresfor EBL are missed.

Referring again to FIGS. 2 and 5, the CFE algorithm 10 is shown. Thefeature detection function 22 may incorporate a process that includesillumination-invariant feature detection 60. This step is used to detectcandidate features to be passed on to the feature classificationfunction 24 and the process round lights function 26 and the processmerged lights function 28 and the process skylights function 30. Anillumination-invariant low-level feature detector 60 utilizingdifferential images is able to overcome shortcomings of image intensitythresholding, such as those explained above for Otsu's method, bydetecting feature candidates at different scales as local maxima indifferential image space.

Referring collectively to FIGS. 4A and 4B, input images 500 and 502 of aceiling 112 can be seen as processed by Otsu's method for EBL purposes.In FIG. 4A the left side of the image shows the raw input image 500, andthe right side depicts the features extracted by Otsu's method where theskylights 116 have been completely ignored by the processor. In FIG. 4Bthe right hand section shows the presence of several skylights 116 inthe raw input image 502, however, after processing only the brightestskylight 116 is detected as shown in the right side of FIG. 4B.Skylights 116 are the most difficult form of warehouse lighting 114because the illumination levels vary significantly with time of day andweather conditions. Additionally, sunlight radiance can be unevenlyspread across the scene, leaving even a single skylight 116 with bothbright and dim sections.

Referring collectively to FIGS. 2 and 5, the feature detection function22 can comprise an illumination-invariant feature detection function 60for low-level feature detection. For example, candidate features can beextracted from the skylights 116 or round lights 118. Theillumination-invariant feature detection function 60 can enable the EBLto perform robustly for skylight-heavy sites in which illuminationlevels are highly variable. Accordingly, instead of relying on abi-modal system, such as Otsu's method, the illumination-invariantfeature detection function 60 can detect light candidate features 416and skylight candidate features 426 at different scales as local maximain differential image space.

Referring now to FIG. 5, a flow chart of a sequence of functions for theillumination-invariant feature detection function 60 is schematicallydepicted. It is noted that, while the functions are enumerated andperformed in a particular sequence in the depicted embodiment, thefunctions can be performed in an alternative order without departingfrom the scope of the present disclosure. It is furthermore noted thatone or more of the functions can be omitted without departing from thescope of the embodiments described herein. Alternatively oradditionally, the illumination-invariant feature detection function 60can be performed independent of the CFE algorithm 10. For example, inputfor the illumination-invariant feature detection function 60, which isdescribed in greater detail herein, can be provided via equivalentprocesses that are performed at a different time, via a differentprocessor as the CFE algorithm 10, or both.

Referring collectively to FIGS. 4A, 4B, 5, 6, 9, and 10, theillumination-invariant feature detection function 60 can comprise abuild pyramid function 402 for processing the input image 200 of theceiling 112 prior to executing further functions on the input image 200by the illumination-invariant feature detection algorithm 60. The buildpyramid function 402 runs a cascading series of image smoothing andsubsampling operations to create a Gaussian scale space pyramid 404 ofthe form shown in FIG. 6, which in some embodiments may be anapproximate Gaussian space pyramid. Images with large scale are thosethat have been smoothed more; a Gaussian blur is a low pass filter soimages with more smoothing only retain coarse, large features. The scalespace construction might not involve subsampling, but it may benecessary for a real-time implementation on low power hardware. Putanother way, the supplemental subsampling operations may be implementedconditionally as a function of available navigation module computingpower. The Gaussian smoothing operations may also be approximated byconvolution with binomial filter kernels for performance reasons. Thebuild pyramid outputs the approximate Gaussian scale space pyramid 404,to be filtered and processed by the illumination-invariant featuredetection algorithm 60.

The illumination-invariant feature detection function 60 can comprisedifferential filtering 406 for calculating determinant of Hessian andtrace of Hessian response pyramids 408, 418 of the size and structure ofthe scale space pyramid 404, for ceiling lights 114 and skylights 116,respectively. Specifically, the differential filtering 406 forms theresponse pyramids 408, 418 to distinguish between ceiling lights 114 andskylights 116. Differential filtering 406 takes as input an approximateGaussian scale space pyramid 404 from an input image 200 of a warehouseceiling 112 comprising ceiling objects (ceiling lights 114, skylights116). The differential filtering 406 is applied to calculate adeterminant of Hessian response for each image within the approximateGaussian scale space pyramid 404 to build a determinant of Hessianresponse pyramid 408. The differential filtering 406 is applied tocalculate a trace of Hessian response for each image within theapproximate Gaussian scale space pyramid 404 to build a trace of Hessianresponse pyramid 418. The response pyramids 408, 418 are calculated suchthat they are of the same size and structure as the approximate Gaussianscale space pyramid 404. The response pyramids 408, 418 are used toidentify ceiling object candidates, i.e., by using the determinant ofHessian response pyramid 408 to find ceiling light candidates 416 andthe trace of Hessian response pyramid 418 to find skylight candidates426. For example, ceiling light candidates are subjected to ceilinglight candidate feature processing to identify valid ceiling lights 114in the warehouse 110. In another example, skylight candidates aresubjected to skylight candidate feature processing to identify validskylights 116 in the warehouse 110. The determinant of Hessian responsesuppresses elongated structures in an image 200, like skylights 116, andtherefore is more suitable for identifying circularly symmetricfeatures, like round ceiling lights 114. The trace of Hessian responsedoes not suppress elongated structures in an image and can be used toidentify skylights 116 or other similar elongated features in an image200.

It is noted that each scale space image in the scale space pyramid 404,produced by the build pyramid function 402, is convolved with thesecond-order partial derivative filter kernels, as shown in theequations:

$L_{xx} = {{\begin{matrix}\left\lbrack 1 \right. & {- 2} & {{\left. 1 \right\rbrack*L\mspace{14mu}{and}\mspace{14mu} L_{xy}} =}\end{matrix}\begin{bmatrix}{{- 1}/4} & 0 & {1/4} \\0 & 0 & 0 \\{1/4} & 0 & {{- 1}/4}\end{bmatrix}}*L{\mspace{11mu}\;}{and}}$ $L_{yy} = {\begin{bmatrix}1 \\{- 2} \\1\end{bmatrix}*{L.}}$

According to the second order partial derivative kernels of theequations, L is a scale space image from the scale space pyramid 404and * denotes convolution of the filter kernel with L to produce thedifferential image. The computation of these three differential imagesmeans that the Hessian matrix is known, as shown below:

${H(x)} = \begin{bmatrix}{L_{xx}(x)} & {L_{xy}(x)} \\{L_{xy}(x)} & {L_{yy}(x)}\end{bmatrix}$

From this, the scale normalized determinant 408 and trace 418 responsesof the Hessian matrix can be calculated for each scale space image inthe scale space pyramid 404, as per the equations:DET=σ₁ ²(L _(xx) L _(yy)(x)−L _(xy) ²(x))TR=σ ₁(L _(xx) +L _(yy))

Here, multiplication by the variance, σ², and standard deviation, σ, ofthe Gaussian smoothing of the present scale space image is required toscale-normalize the resultant responses and account for the decreasingmagnitude of values in a highly smoothed image. This allows fordifferential responses from neighboring scale images to be compareddirectly. The subtraction of the mixed second-order partial derivativeL_(XY) term in the determinant 408 has the effect of suppressingresponses to objects characterized by different rates of image intensitychange along longitudinal and transverse axial directions, i.e., longobjects like skylights 116. Circularly symmetric lights 118 have thesame rate of change in both axes and give strong responses. It for thisreason the determinant 408 of Hessian response is then used to findround light candidate features 416, as shown in FIG. 5. Alternatively oradditionally, the trace 418 of Hessian responds to both circularlysymmetric blobs and longer ridge-like blobs, so it can be used to findskylight candidate regions.

Referring collectively to FIGS. 5, 7 and 9, the illumination-invariantfeature detection function 60 can comprise the multiscale non-maximumsuppression function 410 for round light 118 detection. Here, thedetection is similar to standard computer vision interest pointdetectors. Specifically, local maxima are located within a 3×3×3 window440 in the scale and spatial dimensions. Accordingly, for each pixel 532in the determinant image, all points that are not maximal when comparedto their 26 neighbors in 3×3 pixel regions in the current and adjacentscale images are suppressed. FIG. 7 illustrates this 3×3×3 window. It isnoted that, for clarity, the subsampling operations used in the pyramidimplementation of scale space means that exact scale neighbors cannot befound if the coarser scale image neighbor is of smaller size/resolution.Additionally, an absolute minimum threshold is applied to preventexcessive noisy false positive responses. As shown in FIG. 7, the localmaximum pixel 534 in the window 530 is retained as a local maximum inthe determinant of Hessian response if it is maximal with respect to itsneighbor pixels 532 in scale and space

Referring to FIG. 9, the illumination-invariant feature detectionfunction 60 can comprise the filter keypoints function 412 for removingcandidate points that do not likely correspond to ceiling lights, suchas ceiling lights that do not meet a threshold. Despite the multiscalenon-maximum suppression function 410, many candidate points can bereturned, hundreds if the threshold is set too low and is picking outdim lights. It is necessary to filter this set down to those that likelyto actually correspond to ceiling lights. Specifically, it is notedthat, all determinant of Hessian maxima with a negative trace of Hessianvalue are retained as they correspond to bright blobs on a darkbackground, representing lights, and those with a positive value,corresponding to dark blobs, are removed. The filter keypoints function412 may then use several other filters on the candidate points to checkagainst empirically set thresholds. These may include the scales atwhich the keypoints were detected, the spatial location of the keypoint,the magnitude of the determinant value at the keypoint location, and,some average trace of Hessian values of the surrounding area.Alternatively or additionally, a trained classifier using statisticaland machine-learning techniques may be utilized. Such a classifier mayuse the properties described above as feature descriptors or,alternatively or additionally, it may utilize other established featuredescriptors such as Haar-like features, histogram of gradients or thelike.

Referring to FIG. 9, the illumination-invariant feature detectionfunction 60 can comprise the refine keypoints function 414 for refiningthe spatial position and scale of keypoints believed to be ceilinglights. Specifically, this can be done using quadratic interpolationacross each of the x, y and scale dimensions, the specifics of which maybe gleaned from the computer vision feature detection art. The refinekeypoints function 414 outputs the ceiling light candidate features 416for further processing by the CFE algorithm 10.

Referring to FIG. 10, the illumination-invariant feature detectionfunction 60 can comprise the sum large-scale trace of Hessian imagesfunction 420 for summing a selection of trace response images from thetrace response pyramid 418 into an integrated trace response image,within which skylight 116 regions are searched for. It is contemplatedthat the skylight detection may consist of a cross-scale search such asthe point/light candidate detection process used to output the ceilinglight candidate features 416. Determining the scale space line or ridgeis more complex than a determining single point. The merging of multiplecoarse-scale trace images (coarse scale because the skylights 116 aregenerally much larger features than the round ceiling lights 118) helpsto account for some variation in skylight size and ultimately simplifiesthe remaining processing down to a single-image-processing probleminstead of a whole pyramid 418.

Referring to FIG. 10, the illumination-invariant feature detectionfunction 60 can comprise the threshold integrated trace responsefunction 422 for further processing the trace response 418.Specifically, the trace response 418 is smoothed and a threshold isapplied. It is contemplated that the threshold may or may not be fixed.Referring collectively to FIGS. 8A, 8B, and 10, theillumination-invariant feature detection function 60 can comprise theconnected component filtering function 424 for extracting and filteringconnected components. The connected components can be extracted from thebinary thresholded integrated trace of Hessian image and may filteredfor size and aspect ratio, or other suitable properties, to selectsubstantially rectangular regions. In some embodiments, the trace ofHessian response is utilized in a connected component filtering 424function for extracting and filtering connected components that areextracted from the binary thresholded integrated trace of Hessianresponse image.

The substantially rectangular regions that pass the filtering criteriacan be represented by oriented bounding box regions of interest 426 andwill be passed on to the existing process skylight algorithm 30 andpoint fix extraction algorithm 40. Further, it is contemplated thatadditional validation or filtering between light candidates and skylightcandidates may be employed after the filtering to remove falsepositives. The input image 500 of FIG. 4A is shown in FIG. 8A as havingbeen processed by the illumination-invariant feature detection function60. The feature candidates have been detected at a much highersensitivity than without the illumination-invariant feature detectionfunction 60. Likewise, the input image 502 of FIG. 4B is shown in FIG.8B as having been processed by the illumination-invariant featuredetection function 60. Again, the sensitivity of the detection hasincreased for feature candidates.

Referring collectively to FIGS. 2, 6 and 9, at the completion of thefeature detection function 22, which may be implemented as anillumination-invariant feature detector 60, low-level candidate features416 and 426 are made available for further processing, for example, bythe process round lights function 26 and process skylights function 30.In some embodiments a feature classification step 24 may be employed tofurther distinguish between low-level candidate features correspondingto other ceiling light types 114, such as merged lights. Additionally oralternatively, the feature classification step 24 may be employed toclassify undesirable false positive feature candidates, such as thosecaused by reflections, as a noise class and thereafter discard thesefrom contention for further processing. The feature classification step24 may be implemented as a trained classifier using statistical andmachine-learning techniques and may incorporate or exist in addition tothe classifier process described for the filter keypoints function 412.

Referring collectively to FIGS. 3, 10, 11, and 12, a flow chart of asequence of functions for the process skylights function 30 isschematically depicted. It is noted that, while the functions areenumerated and performed in a particular sequence in the depictedembodiment, the functions can be performed in an alternative orderwithout departing from the scope of the present disclosure. Input to theprocess skylights function 30 can be skylight candidate regions 426 inthe form of oriented bounding rectangles. The skylight candidate regions426 can be located by the illumination-invariant feature detectionalgorithm 60. The output from the process skylights function 30 can beskylight features which may consist of centerline features, andalternatively or additionally, skylight panel corners 130 or point fixfeatures. These skylight features may eventually form part of the set offeatures 54 reported to the remainder of the EBL localization algorithmto determine the position of the vehicle 100 inside the warehouse 110.

Referring collectively to FIGS. 11-15, the process skylights function 30can comprise a create edge image function 32 for transforming orientedbounding boxes 426 into binary edge images 436. The oriented boundingboxes are the skylight candidate regions 426 located by theillumination-invariant feature detection algorithm 60, and may beoriented at any angle. The create edge image function 32 can compriseany algorithm capable of repeatable transformation of the bounding boxes426 into binary edge images 436. In one embodiment, an axis-alignedbounding box 438 is fit to the oriented bounding rectangle 426 that isthe candidate region as depicted in FIG. 11. The axis-aligned boundingboxes 438 are the smallest axis-aligned rectangles that contain the fourcorners of the oriented bounding boxes 426. Each axis-aligned boundingbox 438 can be used to crop the original full-size image into a seriesof smaller images; one for each skylight candidate region 426. Usingsub-images instead of the full-size image helps improve computationalefficiency by reducing the number of pixels to be processed insubsequent image processing operations. Each sub-image is thentransformed into a binary edge image 436. The binary edge image 436 is asuitable form to input into a Hough transform algorithm. The edge image436 is created using the Canny edge detection algorithm, but may beproduced with an equivalent method for producing binary edge images 436.

In some embodiments, it may be beneficial to the quality of the edgeimages 436 if long bounding boxes 426, detected from skylights 116extending across a substantial proportion of the input image 200, aresplit into two or more bounding boxes 426 prior to the execution ofcreate edge image function 32. For example, in the case of the Cannyedge detection algorithm the low and high thresholds may not be suitablefor generating a good edge image 436 if there is significant variationin the brightness of the skylight 116 across its length. Splitting largebounding boxes 426 into two or more smaller bounding boxes improves thelocality of subsequent processing in the create edge function 32.

Referring collectively to FIGS. 2, 3 and 11, in some embodiments the x-and y-gradient images of the input image 200 can be calculated duringthe create edge image 32 using a first order derivative operator, suchas the Sobel operator. These gradient images are necessary for the Cannyedge detection algorithm but, alternatively or additionally, can be usedto calculate the gradient magnitude and orientation images which may bepreserved for use in processing operations, including but not limited tofiltering operations, in subsequent stages of the process skylightsfunction 30 or CFE algorithm 10. For example, the gradient magnitude andorientation images can be used to reason about the strength anddirection of lines detected in the Hough transform line extractionfunction 34 and therein assist in the removal of inaccurate or otherwiseundesirable lines.

Referring collectively to FIGS. 11-16A, the process skylights function30 can comprise a Hough transform line extraction function 34 fordetermining edge lines 434 from the binary edge images 436. Each edgeimage 436 is processed by the Hough transform line extraction function34 to find edge line segments 434. The skylight candidate region 426corresponding to the edge image 436 can be used to help setup the Houghtransform to predispose the extraction function 34 to return edge lines434 along the longitudinal edges of the skylights 116. Specifically, theangle of the longer edges of the skylight candidate regions 426 can becalculated and a restricted range about this angle can be thresholdedfor in the gradient orientation image to create a binary image mask. Themask can be applied to the binary edge image 436 so that pixels on edgeswith gradient orientation too dissimilar to the desired longitudinaledge lines are suppressed and not input to the Hough transform. Thisincreases computational efficiency as there are fewer pixels to processin the voting stage of the Hough transform and additionally improves thequality of output lines, for example by suppressing lines that are insubstantially perpendicular orientation to the required lines, therebyreducing the need for further line filtering stages after the Houghtransform function 34. Alternatively or additionally, the angle rangethat each pixel is voted through in the Hough transform can be set usingknowledge of the angle of orientation of the skylight candidate regions426 which also improves computational efficiency and helps removeunwanted lines. FIG. 14A shows a possible set of line segments returnedby the Hough transform with the above-described angle rangerestrictions; note that line segments in the substantially perpendicularorientation at the skylight panel separations have not been detected.Additionally, the Hough transform can be set to only return lines abovea certain length, for example, above a set fraction of the length of thecandidate region bounding rectangle 426, as shown in FIG. 14B, where theshorter lines of FIG. 14A have been removed.

The process skylights function 30 can comprise a pair skylight edgesfunction 36 that takes the skylight edge line segments 434 extracted bythe Hough transform line extraction function 34 as input and selects apair of these longitudinal edge line segments 434 for each skylight 116that can later be used to determine the centerline 250. In oneembodiment, the result of the pair skylight edges function 36 is shownin FIG. 15. The longitudinal edge pairing function 36 picks thestrongest line 434 as a first edge and then seeks to locate a pairingline segment 434 that is similar in angle and at distance similar to thewidth of the skylight candidate region 426.

It has been found that this method may return an incorrect result onoccasion; for example if the right-most longest edge segment 434 in FIG.14B is considered to be the first edge segment 246, it may beincorrectly matched to one of the substantially parallel lines runningalong the middle of the skylight. Due to phenomena such as occlusion,for example causing the fractured appearance of the skylight as seen inFIG. 14A-15, and the differences in observed size of skylights 116within and across different warehouses 110, it may be difficult to tuneparameters to always select the correct second pairing edge segment. Inan alternative embodiment, the oriented bounding boxes 436 (that is, theskylight candidate regions) may be assumed to be sufficiently accurateindicators of skylight locations in the image and used to guide the linesegment pairing process. Specifically, for each of the longer edges ofthe rectangular oriented bounding box 426, the corresponding candidateline segments 434 extracted by the Hough transform line extractionfunction 34 can be sorted by distance, in order of nearest to furthest,where the distance may be defined as that between the midpoint of thelong edge of the oriented bounding box 426 and the intersection of aline through the midpoint of and perpendicular to the long edge of theoriented bounding box 426 with the current candidate line segment 434.Subsequently, for each long edge segment of the skylight candidateoriented bounding box 426, the respective winning candidate line segment434 can be selected as that with minimum distance. In cases where thereare multiple line segments 434 within some small distance threshold tothe winning line segment, a non-max suppression routine may be performedthat uses the gradient magnitude image, calculated and preserved at thecreate edge image function 32 stage, in which the line segment 434 withthe greatest average gradient magnitude, or alternatively oradditionally, the greatest cumulative gradient magnitude along the lineis selected. In this embodiment, the longitudinal edge pairing function36 is more likely to consistently select and pair the outside most edgesegments as shown in FIG. 15.

Referring collectively to FIGS. 2, 11, and 14A-16B, the processskylights function 30 can comprise a centerline calculating function 38for calculating a centerline 250 of each skylight 116 from the two edgelines selected by the longitudinal edge pairing function 36.Specifically, a first edge line 246 and a second edge line 248, asselected by the edge pairing function 36, can be utilized to derive thecenterline 250 for each skylight 116.

The centerline 250 can be calculated based upon the first edge line 246and the second edge line 248. For the sake of clarity, it is noted thateach of the first edge line 246 and the second edge line 248 and theresultant centerlines 250 can be specified as line segments (FIG. 9B) oras an infinite lines (FIG. 9A). The method for finding the centerline issimilar for either case—if the first edge line 246 and second edge line248 are provided as line segments, they can be extrapolated and treatedas infinite lines.

Referring collectively to FIGS. 2, 11, 17 and 18, as described in detailabove, an input image 240 of a ceiling 112 with skylights 116 can beprovided. It is noted that the skylights 116 are represented in FIGS. 17and 18 as borders of detected raw skylight features for clarity. Theinput image 240 can be analyzed to determine a first edge line 246 and asecond edge line 248 of the skylights 116. For example, the CFEalgorithm 10, specifically the process skylights function 30 stagesthrough to the longitudinal edge pairing function 36, can be utilized toextract the first edge line 246 the second edge line 248, and associatedend points from the input image 240. The first edge line 246 and thesecond edge line 248 are depicted in FIG. 18 as being overlaid upon theskylights 116. The first edge line 246 and the second edge line 248 canbe longitudinal edge line segments that extend longitudinally along theskylights 116. When extrapolated beyond the depicted longitudinalsegments, the first edge line 246 and the second edge line 248 canconverge at a vanishing point 252, which is located off of the inputimage 240 of FIG. 17. Specifically, the first edge line 246 can beextended by a first extrapolating ray 254 that is aligned with the firstedge line 246. The second edge line 248 can be extended by a secondextrapolating ray 256 that is aligned with the second edge line 248.Each of the first extrapolating ray 254 and the second extrapolating ray256 can be extended to at least the vanishing point 252 to represent theconvergence of the first edge line 246 and the second edge line 248.

The centerline 250 of the skylights 116 can be disposed between thefirst edge line 246 and the second edge line 248. In some embodiments,the centerline 250 can be represented as a continuous line thatoriginates at the vanishing point 252 of the first edge line 246 and thesecond edge line 248 at an angle that bisects the first edge line 246and the second edge line 248. Specifically, the first edge line 246 andthe second edge line 248 can be oriented with respect to one another atan angle φ. Accordingly, the angle of the centerline 250 can be abouthalf of the angle φ measured from the first edge line 246 or the secondedge line 248. It is noted that the first extrapolating ray 254 and thesecond extrapolating ray 256 are provided herein primarily forclarifying the location of the vanishing point 252. Accordingly, itshould be understood that the embodiments described need not make use ofsuch rays for determining the vanishing point 252. Indeed, it may bepreferred to determine the location of the vanishing point 252 directlyfrom the first edge line 246 and the second edge line 248.

Referring collectively to FIGS. 17-19A, the skylight 116 centerlines 250as described as above in the form of infinite lines can be reporteddirectly to the EBL as features to assist in the localization and,alternatively or additionally, navigation of the vehicle 100 or,alternatively or additionally can be transformed into centerlinesegments. Referring to FIG. 19A, the first end point 260 and second endpoint 262 of the first edge line segment 246 can be projected onto theinfinite line representation of centerline 250 in FIG. 18. Similarly thefirst edge point 264 and second edge point 266 of the second edge linesegment 248 can be projected onto the centerline 250. The projected endpoints of the first edge line 246 and the projected end points of thesecond edge line 248 can be paired based upon proximity to the vanishingpoint 252. Specifically, the nearest projected end points to thevanishing point 252, in this case the projections of the first end point260 of the first edge line 246 and the first end point 264 of the secondedge line 248, can be paired and averaged to find the first end point282 demarcating one end of the centerline segment 250. Accordingly, thefurthest projected end points to the vanishing point 252, in this casethe projections of the second end point 262 of the first edge line 246and the second end point 266 of the second edge line 248, can be pairedand averaged to find the second end point 284 demarcating the other endof the centerline segment 250.

Referring to FIG. 16B, the result of the process described above for thecenterline calculating function 38 is shown for the example image 200introduced in FIG. 3. The centerline 250 can be determined from thepaired end points. Specifically, the centerline 250 can be a linesegment demarcated by a first end point 282 and a second end point 284.The first end point 282 of the centerline 250 can be determined from thefirst end point 260 of the first edge line 246 and the first end point264 of the second edge line 248, which as noted above can be paired. Thesecond end point 284 of the centerline 250 can be determined from thesecond end point 262 of the first edge line 246 and the second end point266 of the second edge line 248, which can also be paired. In someembodiments, the first end point 282 and the second end point 284 of thecenterline 250 can be determined by averaging each of the respectivepairs of end points of the first edge line 246 and the second edge line248.

Referring collectively to FIGS. 16A-16B, the centerline 250, the firstedge line 246, the second edge line 248, the corresponding Houghcoordinates (for infinite lines), the corresponding end points (for linesegments), or combinations thereof of each of the skylight candidateregions 426 can be associated with one of the skylights 116, and the EBLcan be utilized to provide features for navigation of the vehicle or canbe presented by a display communicatively coupled to the EBL. It isnoted that there are a number of parameters (e.g. thresholds) that canbe configured for each function of the CFE algorithm according to theparticular site that is being navigated by the vehicle. Accordingly, theembodiments described herein can further include a calibration stage fordetermining the precise values for the parameters.

Referring collectively to FIGS. 1, 17 and 19A, the embodiments describedherein may be utilized in environments with various types and amounts ofceiling lights 114. For example, the ceiling 112 of the warehouse 110can have an insufficient number of round lights 118 or merged lights 120for use as features for navigation. For example, the input image 240 maynot include any round lights 118 or merged lights 120. It has beendiscovered that the EBL may have decreased accuracy when provided with afeature set missing complementary point features extracted from roundlights 118 or merged lights 120. That is, the centerline 250 extractedfrom the skylights 116 may not fully constrain the localization of thevehicle 100 and lead to inaccurate solutions. For example, the EBL maybe unable to accurately determine how far along the centerline 250 thevehicle 100 is positioned.

Referring collectively to FIGS. 1 and 11, the skylights 116 can span asection of the ceiling 112 and be arranged substantially along a row128. In some embodiments, the process skylights function 30 can bemodified such that in addition to extracting centerline features fromthe skylights 116 as an EBL feature, the process skylights function 30can further comprise a point fix function 40 for extracting point fixfeatures. For example, point fix features can be extracted from thecorners 130 of the skylights 116. The point fix features can enable theEBL to perform accurately for skylight-heavy, round-light sparse sites.Accordingly, instead of directly reporting the centerline features 250to the EBL following determination, the process skylights function 30can extract point fix features using the centerline and edge linefeatures as input.

Referring now to FIG. 20, a flow chart of a sequence of functions forthe point fix function 40 is schematically depicted. It is noted that,while the functions are enumerated and performed in a particularsequence in the depicted embodiment, the functions can be performed inan alternative order without departing from the scope of the presentdisclosure. Specifically, in some embodiments, it may be desirable toswitch the order of the angle filtering function 306 and the lengthfiltering function 310. In some embodiments, the angle filteringfunction 306 can be performed prior to the intersection function 308. Itis furthermore noted that one or more of the functions can be omittedwithout departing from the scope of the embodiments described herein.Specifically, the angle filtering function 306, the length filteringfunction 310, or both the angle filtering function 306 and the lengthfiltering function 310 can be omitted. Furthermore, it is noted that theregion of interest function 302 can be omitted without departing fromthe scope of the embodiments described herein. Alternatively oradditionally, the point fix function 40 can be performed independent ofthe CFE algorithm 10 and the process skylights function 30. For example,input for the point fix function 40, which is described in greaterdetail herein, can be provided via equivalent processes that areperformed at a different time, via a different processor as the pointfix function 40, or both.

Referring collectively to FIGS. 11, 19A and 20, at step 300 the pointfix function 40 can receive input features such as, for example, thefirst edge line 246, the second edge line 248, the centerline 250 of theskylights 116, or combinations thereof. As is noted above, each of thefirst edge line 246, the second edge line 248, the centerline 250 can beprovided as line segments. Accordingly, the first edge line 246 can bedemarcated by a first end point 260 and a second end point 262. Thesecond edge line 248 can be demarcated by a first end point 264 and asecond end point 266. The centerline 250 can be demarcated by a firstend point 282 and a second end point 284.

Referring collectively to FIGS. 11, 18 and 20, each of the first edgeline 246, the second edge line 248, and the centerline 250 can beprovided as line segments. In some embodiments, the first edge line 246,the second edge line 248, the centerline 250, and the vanishing point252 of the skylights 116 can be provided by the process skylightsfunction 30 after the centerline calculating function 38 is performed.Alternatively or additionally, the first edge line 246, the second edgeline 248, the centerline 250, and the vanishing point 252 of theskylights 116 can be provided via a separate process and provided asdirect input.

Referring collectively to FIGS. 19A-20, the point fix function 40 cancomprise a region of interest function 302 for selecting a usableportion of the skylights 116 for determining point fix features. Theregion of interest function 302 can be utilized to increase the accuracyand efficiency of the point fix function 40. For example, it has beendiscovered that in sites with relatively high ceiling heights and longrows of lights, the extracted centerlines may not fit to the center ofthe light across the whole image. Such errors may be caused by imagedistortion due to the camera lens, poor selection of edge segments tocalculate the extracted centerline, or both. It has furthermore beendiscovered that the accuracy of the point fix function 40 can beincreased by restricting the area of the skylights 116 with the regionof interest function 302.

Generally, the region of interest 258 can be set to be coincident with ausable portion of the skylights 116, i.e., the area bounded by theregion of interest 258 includes a subset of the skylights 116. In someembodiments, the region of interest 258 can be determined using thecenterline 250 as a reference. Specifically, the centerline 250 can beutilized as an axial datum for the determination of the region ofinterest 258. For example, the region of interest 258 can be generatedby a bounding box that is axis aligned with the centerline 250 anddemarcated by the end points of the first edge line 246 and the secondedge line 248 that bound the largest region. Accordingly, the region ofinterest 258 can be demarcated by the second end point 262 of the firstedge line 246 and the first end point 264 of the second edge line 248,such that opposing corners of the region of interest 258 are set to thesecond end point 262 of the first edge line 246 and the first end point264 of the second edge line 248.

Alternatively or additionally, a region of interest 259 that is morerestrictive than the region of interest 258 can be determined using thecenterline 250 as a reference. Specifically, the first end point 282 andthe second end point 284 of the centerline 250 can be projected upon thefirst edge line 246 and the second edge line 248 to create intersectionpoints 286. Optionally, the first edge line 246 and the second edge line248 can be extrapolated to determine the intersection points 286.Accordingly, the region of interest 259 can be demarcated by one or moreof the intersection points 286. Specifically, opposing corners of theregion of interest 259 can be set to any two of the intersection points286 such as, but not limited to, the intersection points 286 that resultin the largest area, the intersection points 286 that result in thesmallest area, or the intersection points 286 that result in each of theintersection points 286 being coincident with the region of interest259.

Referring collectively to FIGS. 18 and 19B, the region of interest 258can be determined using the vanishing point 252, the first edge line246, and the second edge line 248. For example, the region of interest258 can be determined by calculating the distance between endpoints ofthe first edge line 246 and the second edge line 248 with respect to thevanishing point 252. Specifically, the first end point 260 of the firstedge line 246 can be closer to the vanishing point 252 than the secondend point 262 of the first edge line 246. The first end point 264 of thesecond edge line 248 can be closer to the vanishing point 252 than thesecond end point 266 of the second edge line 248.

In some embodiments, the region of interest 258 can be set between endpoints of the first edge line 246, and the second edge line 248 that arethe maximum distance from the vanishing point 252 and minimum distancefrom the vanishing point 252. Specifically, as depicted in FIG. 18, thesecond end point 262 of the first edge line 246 can be the end point ofthe first edge line 246 and the second edge line 248 that is the maximumdistance from the vanishing point 252. The first end point 264 of thesecond edge line 248 can be the end point of the first edge line 246 andthe second edge line 248 that is the minimum distance from the vanishingpoint 252. Accordingly, the region of interest 258 can be demarcated bythe second end point 262 of the first edge line 246 and the first endpoint 264 of the second edge line 248. For example, the region ofinterest 258 can bound a substantially rectangular area that iscoincident with the skylights 116, such that opposing corners of theregion of interest 258 are set to the second end point 262 of the firstedge line 246 and the first end point 264 of the second edge line 248.

Referring again to FIGS. 18-19B, a region of interest 268 can bedemarcated by an overlapping portion of the first edge line 246 and thesecond edge line 248. Generally, the region of interest 268 is the mostrestrictive region of interest described herein. The overlapping portioncan be defined by the end points of the first edge line 246 and thesecond edge line 248. In some embodiments, the region of interest 268can be defined by an axis aligned bounding box that is demarcated by theend points of the first edge line 246 and the second edge line 248 thatresult in the smallest area. Accordingly, the region of interest 268 canbe include the overlapping portion demarcated by the first end point 260of the first edge line 246 and the second end point 266 of the secondedge line 248.

Alternatively or additionally, the region of interest 268 can bedetermined utilizing the vanishing point 252. Specifically, each of thefirst edge line 246 and the second edge line 248 can each have aproximal end point nearest the vanishing point 252, depicted in FIG. 18as the first end point 260 and the first end point 264. Each of thefirst edge line 246 and the second edge line 248 can have a distal endpoint furthest from the vanishing point 252, depicted in FIG. 18 as thefirst end point 260 and the first end point 264. The region of interest268 can be demarcated by the proximal end point furthest from thevanishing point 252 and the distal end point nearest the vanishing point252. Specifically, as depicted in FIG. 18, the first end point 260 ofthe first edge line 246 can be selected as the proximal end pointfurthest from the vanishing point 252. The second end point 266 of thesecond edge line 248 can be selected as the distal end point nearest thevanishing point 252.

Referring collectively to FIGS. 19B-21B, the point fix function 40 cancomprise a transversal line extraction function 304 for extractingtransversal edges 270 from the skylights 116. The transversal lineextraction function 304 can comprise the feature extraction algorithm,described herein above, such as, but not limited to, the HoughTransform. The transversal line extraction function 304 can beconfigured to extract line segments from the skylights 116 that have avertical component with respect to the centerline 250. That is theextracted line segments can be characterized by a vector having acomponent that is substantially orthogonal to the centerline 250. Forexample, the Hough transform can be applied with appropriate parametersto detect transversal edges 270 of the skylights 116.

In some embodiments, the transversal edges 270 that are coincident withthe region of interest 258 can be preferred. For example, thetransversal line extraction function 304 may only extract lines from theportion of the skylights 116 that are within the region of interest 258.Alternatively or additionally, the transversal line extraction function304 may operate on skylights 116 outside of the region of interest 258and filter the extracted lines to obtain only transversal edges 270 thatare coincident with the region of interest 258. Accordingly, thetransversal edges 270 of the portion of the skylights 116 that arecoincident with the region of interest 258 can be detected by thetransversal line extraction function 304. It is noted that, for clarity,the transversal edges 270 are depicted as being overlaid upon theskylights 116 in FIG. 21A and the transversal edges 270 are depicted inisolation in FIG. 21B.

Referring collectively to FIGS. 11, 12, 13, 19B, 20, 21A and 21B, inembodiments where the transversal line extraction function 304 comprisesthe Hough transform, the skylight candidate region 426 corresponding tothe binary edge image 436 input to the Hough transform can be used tohelp setup the Hough transform to predispose the transversal lineextraction function 304 to return transversal edges 270 substantiallyorthogonal to the centerline 250. Specifically, this process is similarto that used to help setup the Hough transform used in the Houghtransform line extraction function 34 comprised in the process skylightsfunction 30 to be predisposed to extracting edge lines along thelongitudinal edges of the skylights 116. If the gradient orientationimage has been calculated and preserved in the create edge imagesfunction 32, or alternatively or additionally provided via a separateprocess and provided as direct input, a restricted angle range centeredaround the angle of the shorter edges of the skylight candidate region426 can be thresholded for in the gradient orientation image to create abinary image mask. The mask can be applied to the binary edge image 436so that pixels on edges with gradient orientation substantiallydissimilar to the desired transversal edge lines 270 are suppressed andnot input to the Hough transform. This increases the computationalefficiency of the transversal line extraction function 304 as there arefewer pixels to process in the voting stage of the Hough transform andadditionally improves the quality of the output lines, for example bysuppressing lines that are of substantially perpendicular orientation tothe required lines, thereby reducing the need for further line filteringstages after the transversal line extraction function 304. Alternativelyor additionally, the angle range that each pixel is voted through in theHough transform can be set using knowledge of the angle of orientationof the shorter edges of the skylight candidate regions 426 which alsoimproves computational efficiency and helps remove unwanted lines. It isnoted that in the above description utilizing the angle of skylightcandidate region 426 to improve the Hough transform that alternativelyor additionally the angle of one of the more restrictive regions ofinterest 258, 259 or 268 may instead be utilized to achieve the sameoutcome.

Referring collectively to FIGS. 11, 12, 13, 20, 22A and 22B, in someembodiments the transversal line extraction function 304 can comprise aHough transform and, in embodiments where the gradient orientation imagehas been made available by the create edge images function 32 or via aseparate process, the transversal line extraction function 304 can bepredisposed to return transversal edges 270 which are of the desiredorientation and quality to be considered usable edges 272 for subsequentprocessing. Alternatively or additionally, the point fix function 40 cancomprise the angle filtering function 306 for classifying thetransversal edges 270 as usable edges 272 and unusable edges 274.Specifically, it is noted that, while the transversal line extractionfunction 304 can be configured to primarily detect transversal edges 270within a range approximately perpendicular to the angle of thecenterline 250, some incorrect or undesirable lines may be detected.Accordingly, it may be desired to remove unusable edges 274 from thetransversal edges 270. The unusable edges 274 can be the transversaledges 270 having an angle with respect to the centerline 250 that isoutside of a restricted range of angles on either side of the averageangle of all of the transversal edges 270. Alternatively oradditionally, the usable edges 272 can be the transversal edges 270having an angle with respect to the centerline 250 that is within therestricted range of angles on either side of the average angle of all ofthe transversal edges 270 with respect to the centerline 250. It isnoted that, for clarity, the transversal edges 270 are depicted as beingoverlaid upon the skylights 116 in FIG. 22A and the transversal edges270 are depicted in isolation in FIG. 22B.

Referring collectively to FIGS. 20 and 23, the point fix function 40 cancomprise the intersection function 308 for determining points ofconvergence between the transversal edges 270 and the first edge line246, the transversal edges 270 and the second edge line 248, or both. Inembodiments where the transversal edges 270 are classified as usableedges 272, the transversal edges 270 that are classified as usable edges272 can be utilized to determine points of convergence. The points ofconvergence can be determined by extrapolating the transversal edges270. Specifically, the transversal edges 270 can be extrapolated byextending each of the transversal edges 270 along their length at leastuntil the extrapolation intersects with the first edge line 246, thesecond edge line 248 or both the first edge line 246 and the second edgeline 248. Optionally, the first edge line 246 and the second edge line248 can be extrapolated to determine the points of intersection. Each ofthe points of convergence can be selected as a point fix 280.

Referring collectively to FIGS. 20, 22A, 22B and 23, the point fixfunction 40 can comprise the length filtering function 310 for filteringthe transversal edges based upon a length ratio. The length filteringfunction 310 can be utilized to remove the transversal edges 270 that donot cross a panel of the skylights 116 to a desired extent. For example,each transversal edge 270 can be compared to corresponding points ofconvergence, i.e., the points of convergence that were determined fromthe transversal edge 270. Specifically, any transversal edges 270 with alength ratio below a threshold level may be removed, where the lengthratio is given by the ratio of length (transversal edge): length (spanbetween points of convergence). Accordingly, the point fixes 280 can beselected from the points of convergence of the remaining transversaledges 270, i.e., the transversal edges 270 that were not removed. It isnoted that the length filtering function 310 was utilized to ensure thatnone of the point fixes 280 was selected from the short edges 276, i.e.,the transversal edges 270 classified as short edges 276 were removedfrom consideration for point fixes 280.

The point fix function 40 can comprise a feature reporting function 312for reporting the extracted features to the EBL. Specifically, thefeature reporting function 312 can report the centerline 250 and thepoint fixes 280 to the EBL for use for navigation. Specifically, the EBLcan compare the centerline 250 and the point fixes 280 to correspondingfeatures in a map for navigational purposes. Additionally, for eachreported point pair (i.e., derived from the one of the transversal edges270) of the point fixes 280, a flag could be added denoting whether thepoint fix 280 sits on a transition of dark (between panels of theskylights 116) to bright (panels of the skylights 116) pixels or brightto dark pixels. The flag can be utilized to increase the accuracy of theEBL and reduce the chance of aliasing between an adjacent pair ofpoints. The flag labeling of dark-to-bright versus bright-to-darktransitions could be achieved by scanning along the centerline 250 andaveraging a set of points on either side of each point fix 280 to detectwhether it is a dark or bright patch.

Additionally, in some embodiments, a non-maximum suppression process maybe executed upon the remaining transversal edges 270 prior to thereporting of the point fixes 280 to the EBL. The purpose of thenon-maximum suppression function is to select a single transversal edge270 when a plurality of such have been detected from a single physicalskylight 116 panel edge in the image 240. If the transversal edges 270are sorted by their distance to an end point of the skylight centerline250, a non-maximum suppression window can be defined such that for eachremaining transversal edge 270 for which other transversal edges 270 arefound within the distance defined by the window, the non-maximumsuppression function can be executed to select only the strongest edgeand suppress all others. In one embodiment the strength of thetransversal edge 270 can be defined as the difference between theaverage pixel intensity of the sampled patches either side of thetransversal edge 270 as described above for the setting of the flagdenoting the nature of the edge transition being of a dark-to-brighttype or bright-to-dark type. Alternatively or additionally, the strengthcan be inferred directly from the gradient magnitude image if it hasbeen made available by the create edge image function 22 or provided bysome separate process as direct input. In this case the average gradientmagnitude, or alternatively or additionally the cumulative gradientmagnitude, along the transversal edge 270 can indicate the relativestrength of the edge line segment, where a higher gradient magnitudesuggests a better fit along the physical edge between skylight 116 andbackground ceiling 112.

Referring collectively to FIGS. 11, 17, 20 and 23, the feature reportingfunction 312 may comprise a process that attaches a flag to each pointfix 280 indicating whether the transversal edge 270 comprising the pointfix 280 lies on a dark-to-bright or bright-to-dark edge transition inthe image 240. As described above, one possible method to set this flagmay be to average the pixel intensities of a set of pixels along scanlines, which may include the centerline 250, either side of thetransversal edge 270 and the side with the higher average intensity canbe determined to be the bright side and the side with the lower averageintensity can be determined to be the dark side. Alternatively oradditionally, in embodiments where the gradient orientation image hasbeen computed in the create edge image function 32 of the processskylights function 30, or is provided by a separate process, thedecision of whether a transversal edge 270 and its respective pointfixes 280 lie on a dark-to-bright or bright-to-dark transition can beachieved by a simple look-up and averaging of the gradient orientationvalues along the pixel locations that form the transversal edge 270within the gradient orientation image. The gradient orientation image iscalculated from first order derivative images in the x and y directionsand so the phase angles of the gradient orientation directly indicatethe direction of transition between dark and bright regions of the inputimage 240.

Referring collectively to FIGS. 11, 12, 13, 20 and 23, it is noted thatthe process comprised within the feature reporting function 312 fordetermining whether a transversal edge 270 and its point fixes 280belong to a dark-to-bright class or bright-to-dark class mayalternatively be performed at a different stage within the point fixfunction 40 without altering the scope or result of the embodiment.Indeed in some embodiments it may be preferred to execute thiscomputation as part of the extract transversal lines function 304. Inthis case, knowledge of the angle of the longer edges of the skylightcandidate regions 426 and that the two transversal edge transitionclasses are ninety degrees out of phase either side of this angle can beutilized to threshold the gradient orientation image to produce twobinary image masks. These masks can be applied to the binary edge image436 to produce two separate masked input images for the Hough transformused in the extract transversal lines function 304, where one of themasked input images suppresses pixels that are too dissimilar to thegradient orientation of dark-to-bright transversal edges 270 and therebypredisposes the Hough transform to return transversal edges 270 of thedark-to-bright class, and the other masked input image suppresses pixelsthat are too dissimilar to the gradient orientation of bright-to-darktransversal edges 270 and thereby predisposes the Hough transform toreturn transversal edges 270 of the bright-to-dark class. In thisscenario, the flag indicating whether an extracted transversal edge 270lies on a dark-to-bright or bright-to-dark transition can be set in theextract transversal lines function 304 and known for the remainder ofthe point fix function 40 and alternatively or additionally utilized toassist in other processing within any subsequent functions comprisedwithin the point fix function 40.

Referring collectively to FIGS. 1 and 23, the skylights 116 may beseparated by structural objects of the warehouse 110 having a finitedepth such as, for example, cross beams between the skylights 116.Accordingly, the structural objects may occlude the true corners 130 ofthe skylights 116, i.e., the structural object may be located betweenthe camera 102 and the skylight 116. In some embodiments, it may bepreferred for the EBL to use the point fixes 280 for which the camera102 has direct line of sight. Thus, the EBL can utilize point fixes 280and associated flags to determine whether it is likely that the pointfix 280 corresponds to a corner 130 of a skylight 116 or is occluded.

Referring collectively to FIGS. 2 and 6, the CFE algorithm 10 cancomprise a process round lights function 26 for extracting centroidsfrom round light feature candidates 416 from the input image 200 of theceiling 112. In one embodiment, the round light candidates 416 can beproduced by a feature detection function 22, which can comprise andillumination-invariant feature detection function 60, and filtered by afeature classification function 24. Alternatively or additionally theround light candidates 416 can be provided by separate process as directinput. In some embodiments, the CFE algorithm 10 may comprise a processmerged lights function 28 to extract centroids from low-level candidatefeatures that have been identified as belonging to a merged light class.Centroid features extracted by the process round lights function 26 andthe process merged lights 28 can be combined with centerline features250 and point fix features 280 and this collection or any subset thereofcan be reported by the report features step 54 to the EBL to be utilizedfor assisting in the localization or navigation of the vehicle 100within the warehouse 110. Prior to the report features step 54, the CFEalgorithm 10 can comprise a filter features by region of interestfunction 50 that can remove features outside of a defined region ofinterest. Accordingly, any remaining features, such as reflections froma truck mast, can be removed. Additionally, the convert coordinateframes function 52 can convert feature coordinates from an imageprocessing frame (e.g., origin in top-left corner) to EBL frame (e.g.,origin in center of image) before the features reported 54 are publishedto the EBL.

It should now be understood that embodiments described herein can beutilized to navigate the industrial vehicle through the warehouseutilizing an illumination-invariant feature detection. Moreover, theembodiments described herein can have particular utility in buildings,e.g., warehouses that are round light sparse or lighted primarily byskylights and natural sources of illumination. Accordingly, theillumination-invariant feature detection described herein can beutilized to increase the environmental efficiency of a warehouse. Forexample, during day light hours the use of electricity can be reducedbecause the industrial vehicles described herein can be navigatedthrough the use of naturally illuminated skylights. Moreover, theembodiments described herein can increase the robustness of the EBLthrough the use of illumination-invariant feature detection. Moreover,the increased robustness of the EBL can reduce the environmental costsof operating a warehouse with industrial vehicles, i.e., mileage causedby localization error can be mitigated, which can reduce the powerconsumed by the industrial vehicle and the maintenance costs of theindustrial vehicle.

For the purposes of describing and defining the present invention, it isnoted that reference herein to a characteristic of the subject matter ofthe present disclosure being a “function of” a parameter, variable, orother characteristic is not intended to denote that the characteristicis exclusively a function of the listed parameter, variable, orcharacteristic. Rather, reference herein to a characteristic that is a“function” of a listed parameter, variable, etc., is intended to be openended such that the characteristic may be a function of a singleparameter, variable, etc., or a plurality of parameters, variables, etc.

It is also noted that recitations herein of “at least one” component,element, etc., should not be used to create an inference that thealternative use of the articles “a” or “an” should be limited to asingle component, element, etc.

It is noted that recitations herein of a component of the presentdisclosure being “configured” or “programmed” in a particular way, toembody a particular property, or to function in a particular manner, arestructural recitations, as opposed to recitations of intended use. Morespecifically, the references herein to the manner in which a componentis “configured” or “programmed” denotes an existing physical conditionof the component and, as such, is to be taken as a definite recitationof the structural characteristics of the component.

It is noted that terms like “preferred,” “commonly,” and “typically,”when utilized herein, are not utilized to limit the scope of the claimedinvention or to imply that certain features are critical, essential, oreven important to the structure or function of the claimed invention.Rather, these terms are merely intended to identify particular aspectsof an embodiment of the present disclosure or to emphasize alternativeor additional features that may or may not be utilized in a particularembodiment of the present disclosure.

For the purposes of describing and defining the present invention it isnoted that the terms “substantially” and “approximately” are utilizedherein to represent the inherent degree of uncertainty that may beattributed to any quantitative comparison, value, measurement, or otherrepresentation. The terms “substantially” and “approximately” are alsoutilized herein to represent the degree by which a quantitativerepresentation may vary from a stated reference without resulting in achange in the basic function of the subject matter at issue.

Having described the subject matter of the present disclosure in detailand by reference to specific embodiments thereof, it is noted that thevarious details disclosed herein should not be taken to imply that thesedetails relate to elements that are essential components of the variousembodiments described herein, even in cases where a particular elementis illustrated in each of the drawings that accompany the presentdescription. Further, it will be apparent that modifications andvariations are possible without departing from the scope of the presentdisclosure, including, but not limited to, embodiments defined in theappended claims. More specifically, although some aspects of the presentdisclosure are identified herein as preferred or particularlyadvantageous, it is contemplated that the present disclosure is notnecessarily limited to these aspects.

It is noted that one or more of the following claims utilize the term“wherein” as a transitional phrase. For the purposes of defining thepresent invention, it is noted that this term is introduced in theclaims as an open-ended transitional phrase that is used to introduce arecitation of a series of characteristics of the structure and should beinterpreted in like manner as the more commonly used open-ended preambleterm “comprising.”

What is claimed is:
 1. An industrial vehicle comprising a drivemechanism, a steering mechanism, a vehicle controller, a camera, and anavigation module, wherein: the camera is communicatively coupled to thenavigation module and is configured to capture input images of a ceilingin a warehouse; the vehicle controller is responsive to commands fromthe navigation module; the drive mechanism and the steering mechanismare responsive to commands from the vehicle controller; and thenavigation module is configured to distinguish between object types inthe input images by executing machine readable instructions to createGaussian scale space pyramids from the input images of the warehouseceiling, build determinant of Hessian response pyramids from theGaussian scale space pyramids, build trace of Hessian response pyramidsfrom the Gaussian scale space pyramids, utilize the determinant ofHessian response pyramids to identify candidate object types in theinput images that are characterized by different rates of imageintensity change along a plurality of axial directions, utilize thetrace of Hessian response pyramids to identify candidate object types inthe input images that are characterized by a circularly symmetric rateof image intensity change, subject the identified candidate object typesto candidate feature processing to identify valid object types in thewarehouse, and send commands to the vehicle controller to navigate theindustrial vehicle through the warehouse based upon the valid objecttype identification.
 2. The industrial vehicle of claim 1 wherein theGaussian scale space pyramids are created by running a cascading seriesof image smoothing operations applied to the input images of thewarehouse ceiling.
 3. The industrial vehicle of claim 2 wherein theGaussian scale space pyramids are approximated by convolution withbinomial filter kernels.
 4. The industrial vehicle of claim 1 whereinthe Gaussian scale space pyramids are created by supplementing acascading series of image smoothing operations with subsamplingoperations.
 5. The industrial vehicle of claim 4 wherein thesupplemental subsampling operations are implemented conditionally as afunction of available navigation module computing power.
 6. Theindustrial vehicle of claim 1 wherein the determinant of Hessianresponse pyramids and trace of Hessian response pyramids are calculatedbased on each scale space image being convolved with second-orderpartial derivative filter kernels.
 7. The industrial vehicle of claim 1wherein the determinant of Hessian response pyramids are calculated bysubtraction of a mixed second-order partial derivative term.
 8. Theindustrial vehicle of claim 1 wherein the determinant of Hessianresponse pyramids suppress responses to objects characterized bydifferent rates of image intensity change along longitudinal andtransverse axial directions.
 9. The industrial vehicle of claim 1wherein the determinant of Hessian response pyramids are utilized formultiscale non-maximum suppression wherein local maxima are locatedwithin a window comprising scale and spatial dimensions.
 10. Theindustrial vehicle of claim 9 wherein an absolute minimum threshold isapplied to prevent excessive noisy false positive determinant responses.11. The industrial vehicle of claim 1 wherein the determinant of Hessianresponse pyramids are utilized in a filtering keypoints function forremoving candidate points that do not likely correspond to ceilinglights.
 12. The industrial vehicle of claim 11 wherein the filteringkeypoints function utilizes other filters on candidate points to checkagainst empirically set thresholds.
 13. The industrial vehicle of claim12 wherein the other filters comprise scales at which keypoints aredetected, a spatial location of a keypoint, a magnitude of a determinantof Hessian response pyramid at the keypoint location, a machine-learningclassifier, or an average trace of Hessian response pyramid values of asurrounding area.
 14. The industrial vehicle of claim 1 wherein thedeterminant of Hessian response pyramids are utilized in a refiningkeypoints function for refining spatial position and scale of ceilinglight candidate keypoints.
 15. The industrial vehicle of claim 1 whereinthe trace of Hessian response pyramids are utilized in a sum large-scaletrace of Hessian response images function for summing a selection oftrace of Hessian response images from a trace of Hessian responsepyramid into an integrated trace of Hessian response image.
 16. Theindustrial vehicle of claim 15 wherein skylight regions are searched forwithin a trace of Hessian response pyramid.
 17. The industrial vehicleof claim 1 wherein the trace of Hessian response pyramids are furtherprocessed by a threshold integrated trace of Hessian response function,wherein a trace of Hessian response is smoothed and a threshold isapplied.
 18. The industrial vehicle of claim 17 wherein the threshold isfixed.
 19. The industrial vehicle of claim 17 wherein the threshold isnot fixed.
 20. The industrial vehicle of claim 1 wherein the trace ofHessian response pyramids are utilized in a connected componentfiltering function for extracting and filtering connected componentsbased on a binary thresholded integrated trace of Hessian responseimage.
 21. The industrial vehicle of claim 20 wherein the connectedcomponent filtering function filters for size and aspect ratio to selectrectangular regions.
 22. The industrial vehicle of claim 1 wherein:candidate object types in the input images that are characterized bydifferent rates of image intensity change along a plurality of axialdirections correspond to elongated skylights; and candidate object typesin the input images that are characterized by a circularly symmetricrate of image intensity change correspond to ceiling lights.
 23. Anindustrial vehicle comprising a drive mechanism, a steering mechanism, avehicle controller, a camera, and a navigation module, wherein: thecamera is communicatively coupled to the navigation module and isconfigured to capture input images of a ceiling in a warehouse; thevehicle controller is responsive to commands from the navigation module;the drive mechanism and the steering mechanism are responsive tocommands from the vehicle controller; and the navigation module isconfigured to distinguish between object types in the input images byexecuting machine readable instructions to create Gaussian scale spacepyramids from the input images of the warehouse ceiling, builddeterminant of Hessian response pyramids from the Gaussian scale spacepyramids, build trace of Hessian response pyramids from the Gaussianscale space pyramids, utilize the determinant of Hessian responsepyramids and the trace of Hessian response pyramids to distinguishbetween and identify candidate object types in the input images that arecharacterized by different rates of image intensity change along aplurality of axial directions and candidate object types in the inputimages that are characterized by a circularly symmetric rate of imageintensity change, subject the identified candidate object types tocandidate feature processing to identify valid object types in thewarehouse, and send commands to the vehicle controller to navigate theindustrial vehicle through the warehouse based upon the valid objecttype identification.